Research Methods & Statistics Lesson 2

Mysteries of Life We have questions Why do people behave that way? Is global warming occurring? Will my cancer come back? To get answers, we need… Information  Data Explanation  analysis & interpretation ~

Research Methods & Statistics Integral relationship Must consider both during planning Research Methods How data *are collected What kind of data Statistics Analysis depends on data & how it is collected ~

Scientific Inquiry Observing relationships between variables Variable: any measurable characteristic can take on many different values Science is a way thinking & observing 1. systematic empiricism 2. production of public knowledge 3. examination of solvable problems Guided by theory ~

Theories Descriptions Of observations Explanations Relationships between observations Generate hypotheses testable predictions about relationships ~

The Research Process Systematic Empiricism Observe Explain Predict Test Science is cumulative Theories tested Supported or modified No single experiment conclusive ~

Theories Scientific theories are falsifiable Certain outcomes would disprove them Non-scientific: Salsa dancing is the most exciting form of dance. Scientific: Salsa dancing increases physical fitness ~

Scientific Validity Scientific conclusions About relationships b/n variables Validity Soundness, legitimacy, truth Internal validity About cause & effect External (ecological) validity About broad applicability ~ Example: validity: don’t use thermometer to measure pulse Reliability: checking blood pressure, pulse

How are data collected? 2 scientific approaches Same or similar statistical analysis But not same conclusions Correlational methods Observe co-occurrence of variables Naturalistic observation, case studies, archival research, surveys, etc. Experimental method Manipulate a variable  observe effect on another variable ~

Experimental Variables Independent (IV) Predictor (or cause) Manipulated Dependent (DV) Outcome (or effect) Measured Extraneous variables Or confounding Might also affect outcome (DV) ~

The Experimental Method At least 2 variables: Independent (IV) & Dependent (DV) At least 2 groups (levels of IV) control group - no treatment experimental - receives treatment random assignment to groups Control confounding variables Which might also affect DV Weakens internal validity ~

Variation within an Experiment Systematic Variation due to manipulation of IV Difference between groups Unsystematic Individual differences Variation due to random or uncontrolled variables Potentially confounding variables ~

Variation within an Experiment

Internal Validity Legitimacy of conclusions about cause & effect High internal validity Confident that only changes in IV cause change in DV Low internal validity Confounding variables influence outcome ~ Example: validity: don’t use thermometer to measure pulse Reliability: checking blood pressure, pulse

Two Experimental Approaches Treatment Level of IV or group Between-groups designs AKA Between-subjects, independent groups Each group gets different level of IV Within-subjects designs AKA repeated measures, dependent groups Each individual experiences each level of IV ~

Randomization Important for validity Helps avoid bias Random sampling (or selection) Selection of participants for study Representative sample from population  external validity Random assignment to condition (groups) Minimize biasing of groups  internal validity ~

Correlational vs. Experimental Internal vs External validity Inverse relationship based on control Correlational?  internal vs  external Cannot determine causality Experimental  internal vs  external Establishes cause & effect relationships For useful conclusions need both ~

Planning Research Correlational or experimental research Research design Between-groups or within-subjects Operational definition of variables Data categorical or quantitative Statistical analysis Depends on all of the above ~